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Record W2920879197 · doi:10.1177/0739456x19831064

Unmet Demand for Walkable Transit-Oriented Neighborhoods in a Midsized Canadian Community: Market and Planning Implications

2019· article· en· W2920879197 on OpenAlexafffundabout
Lawrence D. Frank, Jerome Mayaud, Andy Hong, Pat Fisher, Suzanne E. Kershaw

Bibliographic record

VenueJournal of Planning Education and Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsRegional Municipality of WaterlooUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsWalkabilityCommunity designGeographyTravel behaviorSustainabilityPreferenceTransport engineeringSample (material)Built environmentEnvironmental planningEnvironmental healthMedicineEconomicsPolitical scienceEngineeringEcologyCivil engineering

Abstract

fetched live from OpenAlex

Understanding neighborhood preferences remains a key focus for planners. While many studies document the effects of either neighborhood design or neighborhood preference on health and travel behavior, few have explored their combined effect in smaller regions. Using a sample of 2,597 adults in the Region of Waterloo, Ontario, we found an unmet demand for walkable neighborhoods. Results suggest that walkable neighborhoods are independently associated with less vehicle travel after adjusting for sociodemographic and residential preferences. Our study highlights the importance of combining the effects of walkable neighborhoods and preferences for them when addressing health and sustainability goals in suburban communities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.444
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations33
Published2019
Admission routes3
Has abstractyes

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